The tensile stress–strain response of a fiber reinforced concrete dominates the performance under many loading conditions and applications. To represent this property as an average equivalent response, a back-calculation process from flexural testing is employed. The procedure is performed by model fitting of the three-point and four-point bending load deflection data on two types of macro synthetic polymeric fibers, one type of steel fiber and one type of Alkali Resistant (AR) glass fiber. A strain softening tensile model is used to simulate the behavior of different FRC types and obtain the experimental flexural response. The stress–strain model for each age, fiber type and dosage rate is simulated by means of the inverse analysis procedure, using closed-form moment–curvature relationship and load–deflection response of the piecewise-linear material. The method of approach is further applied to one external data set for High Performance Fiber Reinforced Concrete (HPFRC) with two different types of steel fibers and validated by tensile test results reported. Results of back-calculation of stress–strain responses by tri-linear tensile model for all mixtures are compared and correlated with the corresponding standard method parameters used for post crack behavior characterization and a regression analysis for comparative evaluation of test data is presented.

Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively) areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2–3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats' behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task.

Unidirectional glass fiber reinforced polymer (GFRP) is tested at four initial strain rates (25, 50, 100 and 200 s^-1) and six temperatures (−25, 0, 25, 50, 75 and 100 °C) on a servo-hydraulic high-rate testing system to investigate any possible effects on their mechanical properties and failure patterns. Meanwhile, for the sake of illuminating strain rate and temperature effect mechanisms, glass yarn samples were complementally tested at four different strain rates (40, 80, 120 and 160 s^-1) and varying temperatures (25, 50, 75 and 100 °C) utilizing an Instron drop-weight impact system. In addition, quasi-static properties of GFRP and glass yarn are supplemented as references. The stress–strain responses at varying strain rates and elevated temperatures are discussed. A Weibull statistics model is used to quantify the degree of variability in tensile strength and to obtain Weibull parameters for engineering applications.